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All Technical Committee Conferences (All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| HIP, ITE-HI, ASJ-H, VRPSY [detail] | 2022-02-28 13:30 |
Online | Online | [Invited Talk]
Glossiness perception
-- cues, reproduction methods using 3D images and quantitative evaluation, brain mechanism, and effects on facial attractiveness and the neural correlates -- Yuichi Sakano (NICT/Osaka Univ.)  HIP2021-70 |
By virtue of the recent great advances in computer graphics technology, mechanisms for the perception of object material... [more] | HIP2021-70 pp.62-67 |
| MI | 2022-01-27 16:13 |
Online | Online | [Short Paper]
utomatic Extraction of Regions of Vascular Lesions Including Diffuse Lesions in MR Images Using Weakly Supervised Deep Learning Koki Fukaya, Takeshi Hara (Gifu Univ.), Taiki Nozaki, Masaki Matsusako (St.Luke's International Hosp.), Tetsuro Katafuchi (Gifu Medical Univ.), Xiangrong Zhou, Hiroshi Fujita (Gifu Univ.) MI2021-87 |
Klippel-Trenaunay-Weber syndrome (KTS) is a type of vascular lesion for which there is no quantitative diagnostic method... [more] | MI2021-87 pp.186-187 |
| IE | 2022-01-24 13:05 |
Tokyo | National Institute of Informatics (Primary: On-site, Secondary: Online) |
Reduction of Truncation Artifacts by Massive-Training Artificial Neural Network (MTANN) in Fast-Acquisition MRI of the Knee Maodong Xiang, Ze Jin, Kenji Suzuki (Tokyo Tech) IE2021-31 |
MRI has a relatively long acquisition time, leading to patient comfort problems and artifacts from patient motion. Accel... [more] | IE2021-31 pp.21-26 |
| MI | 2021-07-09 14:30 |
Online | Online | Asynchrony analysis of diaphragmatic movement for evaluation of respiratory dynamics in COPD patients Xiao Tan (Chiba Univ.), Yuma Iwao (QST), Chen Ye, Kotaro Takahashi (Chiba Univ.), Yoshitada Masuda (Chiba Univ. Hospital), Ayako Shimada, Naoko Kawata, Hideaki Haneishi (Chiba Univ.) MI2021-20 |
The state of lung movement is an important index for the diagnosis of the chronic obstructive pulmonary disease (COPD). ... [more] | MI2021-20 pp.47-51 |
| MI | 2021-05-17 10:00 |
Online | Online | [Short Paper]
Regression of Induced Electric Field for TMS by using Neural Network and Governing Equation Toyohiro Maki (NITech), Yoshikazu Ugawa, Takenobu Murakami (Fukushima Medical Univ.), Tatsuya Yokota, Akimasa Hirata, Hidekata Hontani (NITech) MI2021-1 |
TMS (Transcranial Magnetic Stimulation) is a method which stimulate the neurons in the brain by using a coil. Since stim... [more] | MI2021-1 pp.1-2 |
| MI | 2021-05-17 14:40 |
Online | Online | MR super-resolution based on signal-image domain learning using phase scrambling Fourier transform imaging Kazuki Yamato, Hiromichi Wakatsuki, Satoshi Ito (Utsunomiya Univ.) MI2021-6 |
In the phase-scrambling Fourier transform (PSFT) imaging, the signals not sampled during imaging can be extrapolated and... [more] | MI2021-6 pp.14-19 |
| MI | 2021-03-15 15:15 |
Online | Online | Deep State-Space Modeling of FMRI Images with Disentangle Attributes Koki Kusano (Kobe Univ.), Takashi Matsubara (Osaka Univ.), Kuniaki Uehara (Osaka Gakuin Univ.) MI2020-59 |
As well as the disorder and other targets, nuisance attributes such as age, gender, and scanner specifications underlie ... [more] | MI2020-59 pp.56-61 |
| MI | 2021-03-15 15:45 |
Online | Online | Feasibility study of automatic extraction method of coronary artery stationary period using CNN
-- Comparison between 1.5T and 3.0T -- Remina Kasai, Yuta Endo, Haruna Shibou, Makoto Amanuma, Kuninori Kobayashi, Shigehide Kuhara (Kyorin Univ.) MI2020-61 |
Magnetic resonance coronary angiography (MRCA) requires data acquisition during the stationary period of the coronary ar... [more] | MI2020-61 pp.66-70 |
| PRMU | 2020-10-09 11:00 |
Online | Online | Examination of data preprocessing in functional MRI image analysis using CNN Yuta Hosoi (Niigata Univ.), Takafumi Hayashi (Nihon Univ) PRMU2020-22 |
fuctional MRI(fMRI) has been used in various fields from medicine and neuroscience to psychology andlinguistics since it... [more] | PRMU2020-22 pp.20-25 |
| MI | 2020-09-03 14:55 |
Online | Online | Performance Improvement of Alzheimer's Disease Classification Using Convolutional Neural Network Daiki Endo, Koichi Ito, Takafumi Aoki (Tohoku Univ.) MI2020-31 |
Alzheimer's disease (AD) is a progressive brain disease that causes a different pattern of brain atrophy from normal agi... [more] | MI2020-31 pp.63-67 |
| IBISML | 2020-03-11 14:10 |
Kyoto | Kyoto University (Cancelled but technical report was issued) |
Accuracy of Brain Tumor Detection and Classification Based on Under Sampled k-Space Signals Tania Sultana, Sho Kurosaki, Yutaka Jitsumatsu, Junichi Takeuchi (Kyushu Univ.) IBISML2019-46 |
The prime concern of Magnetic Resonance Imaging (MRI) is to optimize examination time by assuring a good quality of the... [more] |
IBISML2019-46 pp.91-94 |
| IE, IMQ, MVE, CQ (Joint) [detail] |
2020-03-05 11:10 |
Fukuoka | Kyushu Institute of Technology (Cancelled but technical report was issued) |
Human Motion Recognition from Single Camera Images Using TMRI Cao Jing, Youtaro Yamashita, Joo Kooi Tan (Kyutech) IMQ2019-17 IE2019-99 MVE2019-38 |
In recent years, research on computer vision has progressed and is being applied in a wide range of fields. Among them, ... [more] | IMQ2019-17 IE2019-99 MVE2019-38 pp.17-21 |
| ITE-HI, IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] | 2020-02-27 14:55 |
Hokkaido | Hokkaido Univ. (Cancelled but technical report was issued) |
A Note on Estimation of Image Categories Using Brain Activity While Viewing Images Based on MVBGM-MS Yusuke Akamatsu (Hokkaido Univ.), Ryosuke Harakawa (Nagaoka Univ. of Tech.), Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.) |
This paper presents multi-view Bayesian generative model for multi-subject fMRI data (MVBGM-MS) for accurate estimation ... [more] | |
| MI | 2020-01-29 14:00 |
Okinawa | OKINAWAKEN SEINENKAIKAN | Effects of skull and surrounding area in functional MRI analysis using Convolutional Neural Network Yuta Hosoi, Takafumi Hayashi (Niigata Univ.) MI2019-86 |
A fuctional MRI(fMRI) has been used in various fields from medicine and neuroscience to psychology andlinguistics since ... [more] | MI2019-86 pp.91-96 |
| SP | 2020-01-28 15:50 |
Toyama | Measurement of momentum change in joint range using high-speed rtMRI movie Takuya Asai, Hideaki Kikuchi (Waseda University), Kikuo Maekawa (NINJAL) SP2019-44 |
In our project, a database of the real-time magnetic resonance images (rtMRI) is constructed from FY 2017. It is difficu... [more] | SP2019-44 pp.1-6 |
|
| EMCJ, MW, EST, IEE-EMC [detail] | 2019-10-25 15:05 |
Miyagi | Tohoku Gakuin University(Conf. Room 2, Eng. Bldg. 1) | Modeling Nerve Activation During TMS Targeting Language Area Takashi Sakai (NITech), Keisuike Tani, Satoshi Tnaka (Hamamatsu Univ. School of Medicine), Akimasa Hirata (NITech) EMCJ2019-65 MW2019-94 EST2019-73 |
In recent years, there has been a growing interest in non-invasive stimulation of the brain for medical treatment and di... [more] | EMCJ2019-65 MW2019-94 EST2019-73 pp.163-168 |
| MBE, NC | 2019-10-11 16:20 |
Miyagi | Selection of ultrasmall superparamagnetic particles of iron oxide for vessel size imaging Kazuhiro Nakamura (Akita Noken), Norihiro Katayama (Tohoku Univ), Minoru Osanai (Osaka Univ), Toshibumi Kinoshita (Akita Noken) MBE2019-36 NC2019-27 |
Vessel size imaging (VSI) was already reported based on transverse relaxation time images obtained from spin echo and gr... [more] | MBE2019-36 NC2019-27 pp.37-40 |
|
| MI | 2019-07-05 14:00 |
Hokkaido | Future Univ. Hakodate | [Short Paper]
Automated extraction of cartilage regions on knee MR images by deep learning approach Koki Fukaya, Takeshi Hara, Xiangrong Zhou (Gifu Univ.), Taiki Nozaki, Masaki Matsusako (St. Luke's HP), Hiroshu Fujita (Gifu Univ.) MI2019-17 |
(To be available after the conference date) [more] | MI2019-17 pp.1-2 |
| MBE | 2019-05-19 09:50 |
Niigata | Niigata University | Quantitative evaluation of the femoral condylar shape in the knee joint with the discoid meniscus Takeru Hirano, Toyohiko Hayashi (Niigata Univ), Satoshi Watanabe (Niigata Medical Center) MBE2019-3 |
In order to reduce the load working on the knee joint, two meniscuses are placed as soft tissue on the lateral and media... [more] | MBE2019-3 pp.13-18 |
| MI | 2019-01-22 13:20 |
Okinawa | [Short Paper]
Differences of Segmentation Results by Three Training Data for Cartilage Extraction in Knee MR Images Using Deep Learning Ryoma Aoki, Takeshi Hara (Gifu Univ), Taiki Nozaki, Masaki Matsusako (Dept.of Radiol.,St.Luke's Hosp.), Xiangrong Zhou, Hiroshi Fujita (Gifu Univ) MI2018-76 |
Accurate grasp of cartilage area is important for diagnosis and treatment related to arthropathy diseases. In recent yea... [more] | MI2018-76 pp.63-64 |
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